Automatic extraction of micro-aneurysms and haemorrhages from digital fundus image

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU139968" target="_blank" >RIV/00216305:26220/19:PU139968 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8768847" target="_blank" >https://ieeexplore.ieee.org/document/8768847</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2019.8768847" target="_blank" >10.1109/TSP.2019.8768847</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic extraction of micro-aneurysms and haemorrhages from digital fundus image

Popis výsledku v původním jazyce

In Diabetic Retinopathy, red lesions are consisting of micro-aneurysms and haemorrhages. The paper deals with the proper detection of micro-aneurysms and haemorrhages which are found in fundus images using an automated computer vision. Morphological operations are performed to extract out all the possible candidates that have similar pixel intensity as that of the red lesions. To reject the blood vessels effectively, Gabor filter is used in this paper. Discriminatory features are extracted and fed to train SVM classifier for the proper classification of the micro-aneurysms and haemorrhages. The algorithm developed is tested on 168 fundus images taken from DIARETDBI AND MESSIDOR databases. It achieved an overall accuracy of 93% and 91.8% in classifying the micro-aneurysms and haemorrhage respectively. Proposed work is efficient and the result are encouraging to use in real time applications. © 2019 IEEE.

Název v anglickém jazyce

Automatic extraction of micro-aneurysms and haemorrhages from digital fundus image

Popis výsledku anglicky

In Diabetic Retinopathy, red lesions are consisting of micro-aneurysms and haemorrhages. The paper deals with the proper detection of micro-aneurysms and haemorrhages which are found in fundus images using an automated computer vision. Morphological operations are performed to extract out all the possible candidates that have similar pixel intensity as that of the red lesions. To reject the blood vessels effectively, Gabor filter is used in this paper. Discriminatory features are extracted and fed to train SVM classifier for the proper classification of the micro-aneurysms and haemorrhages. The algorithm developed is tested on 168 fundus images taken from DIARETDBI AND MESSIDOR databases. It achieved an overall accuracy of 93% and 91.8% in classifying the micro-aneurysms and haemorrhage respectively. Proposed work is efficient and the result are encouraging to use in real time applications. © 2019 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

42nd International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-7281-1864-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

249-254

Název nakladatele

Neuveden

Místo vydání

Hungary

Místo konání akce

Budapest, Hungary

Datum konání akce

1. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493442800055